System

Abstract

An object of a system according to an embodiment is to provide means for a visually impaired person to obtain visual information in daily life.SOLUTION: A system according to an embodiment includes a camera, an analysis unit, and a sound output unit. The camera is mounted on the glasses. The analysis unit analyzes the video acquired by the camera. The voice output unit outputs the information analyzed by the analysis unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology limits the means by which visually impaired people can obtain visual information in their daily lives, leaving room for improvement.

[0005] The system according to the embodiment aims to provide a means for visually impaired people to obtain visual information in their daily lives. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an analysis unit, and an audio output unit. The camera is attached to glasses. The analysis unit analyzes the video captured by the camera. The audio output unit outputs the information analyzed by the analysis unit as audio. [Effects of the Invention]

[0007] The system according to the embodiment can provide a means for visually impaired people to obtain visual information in their daily lives. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The visual information support system according to an embodiment of the present invention is a multifunctional system that supports visual information by using AI to analyze images captured by two cameras attached to glasses. This enables the visual information support system to provide multifaceted support for the daily lives of visually impaired people.

[0029] A visual information support system according to an embodiment includes a camera, an analysis unit, and an audio output unit. The camera is attached to glasses and captures video. For example, the camera has high resolution, a wide viewing angle, and a high frame rate. The analysis unit analyzes the video captured by the camera. For example, the analysis unit analyzes the video using an image processing algorithm and extracts information with high accuracy. The audio output unit outputs the information analyzed by the analysis unit as audio. For example, the audio output unit generates high-quality audio using speech synthesis technology. This enables the visual information support system to provide a multifunctional system that supports the daily lives of visually impaired people.

[0030] The analysis unit can analyze text information from video captured by the camera and read it aloud. For example, when the generation AI analyzes text information, the analysis unit understands the context and reads it aloud with appropriate intonation and emotion. For example, it adds a questioning tone to questions and a grateful tone to words of gratitude. The analysis unit also analyzes the structure of sentences and ensures natural reading by taking appropriate pauses for each paragraph. For example, it detects punctuation and line breaks and adds appropriate pauses. The analysis unit also performs sentiment analysis, reading the emotional nuances of the text and reflecting those emotions when reading aloud. For example, it uses a bright tone to express joy and a calm tone to express sadness. This allows visually impaired people to read documents with text information aloud.

[0031] The analysis unit can recognize braille blocks from images captured by the camera and provide audio guidance on their location and direction. For example, when the generation AI analyzes text information, the analysis unit simultaneously analyzes the content of related images and diagrams and explains them audio. For example, it can provide detailed explanations of graphs and diagrams. The analysis unit also uses image recognition technology to identify important elements in images and explain them audio. For example, it can explain people and objects in photographs. The analysis unit can also analyze data in diagrams and read out numerical and statistical information audio. For example, it can explain numbers in tables and data points on graphs. This makes it possible to provide guidance to visually impaired people to move around safely.

[0032] The analysis unit can analyze video of a person captured by a camera and compare it with people registered in advance as learning data. For example, the analysis unit uses a generation AI to analyze the user's emotional state in real time and adjust the speed and tone of the reading based on the results. For example, if the user is relaxed, the reading will be done in a slower tone. The analysis unit also uses an emotion estimation function to emphasize important information and read it earlier if the user is in a hurry. For example, it can quickly convey emergency notifications or important messages. The analysis unit also adjusts the order of the reading content depending on the user's emotional state. For example, if the user is excited, interesting information will be read first. This allows visually impaired people to hear the person matching results when finding acquaintances.

[0033] The analysis unit can analyze the two images captured by the cameras and measure the distance to the object. For example, the analysis unit uses a generative AI to analyze the object's movement and speed and provide dynamic distance information based on that information. For example, it measures the distance to a moving vehicle or pedestrian in real time. The analysis unit also analyzes the object's speed and adjusts the distance information according to that speed. For example, it issues a prompt warning for fast-moving objects. The analysis unit also analyzes the object's movement pattern and provides distance information according to that movement. For example, it predicts the movement of an object moving in a zigzag pattern and measures the distance. This allows visually impaired people to hear the distance to an object when avoiding obstacles.

[0034] The analysis unit can detect objects and situations other than the specified target from the video captured by the camera. For example, when the generation AI detects a foreign object, the analysis unit analyzes the type and danger level of the foreign object and provides detailed information. For example, it determines whether a fallen object is dangerous. The analysis unit also analyzes the type of foreign object and issues an appropriate warning to the user based on that information. For example, it issues a warning if it detects glass shards or a sharp object. The analysis unit also analyzes the danger level of the foreign object and adjusts the strength of the warning based on the results. For example, it issues a strong warning for foreign objects that are highly dangerous. This allows visually impaired people to detect foreign objects while walking and communicate that information via voice.

[0035] In addition to reading out text information, the analysis unit can also explain the contents of related images and charts aloud. For example, when the generation AI analyzes text information, the analysis unit simultaneously analyzes the contents of related images and charts and explains them aloud. For example, it can explain graphs and the contents of diagrams in detail. The analysis unit also uses image recognition technology to identify important elements in images and explain them aloud. For example, it can explain people and objects in photographs. The analysis unit can also analyze chart data and read out numerical and statistical information aloud. For example, it can explain numbers in tables and data points in graphs. This allows the contents of images and charts to be explained aloud when visually impaired people read documents.

[0036] When recognizing the location of a tactile paving block, the analysis unit simultaneously analyzes information about surrounding obstacles and the terrain, allowing it to provide more detailed guidance. For example, when the generation AI recognizes the location of a tactile paving block, the analysis unit simultaneously analyzes information about surrounding obstacles and the terrain, providing detailed guidance. For example, it provides voice guidance about stairs or steps beyond the tactile paving block. When analyzing the location of the tactile paving block, the analysis unit also takes into account information about the surrounding terrain and suggests the optimal route. For example, it provides guidance on routes that avoid slopes and uneven roads. When recognizing the location of a tactile paving block, the analysis unit also analyzes the type and location of surrounding obstacles in detail and provides voice guidance. For example, it detects moving obstacles such as vehicles and bicycles and issues a warning. This makes it possible to provide detailed guidance for visually impaired people to move safely.

[0037] The analysis unit can communicate information to the user using vibrations and tactile feedback in addition to audio guidance. For example, the generation AI communicates information to the user using vibrations and tactile feedback in addition to audio guidance. For example, the location of braille blocks is notified through vibrations. The analysis unit also uses tactile feedback to inform the user of the location and type of obstacle. For example, the strength and pattern of vibrations can be changed to distinguish between different types of obstacles. The analysis unit also combines audio guidance and tactile feedback to provide detailed information to the user. For example, direction guidance is provided through audio and distance is indicated through vibrations. This allows visually impaired people to obtain information not only through audio but also through vibrations and tactile feedback.

[0038] The analysis unit can communicate past conversation history and relationships by voice based on the person matching results. For example, the analysis unit uses a generation AI to communicate past conversation history and relationships by voice based on the matching results. For example, it provides information about previous conversations and mutual acquaintances. The analysis unit also analyzes the relationship between the user and the person being matched based on the matching results and communicates that information by voice. For example, it explains relationships such as between family and friends. The analysis unit also analyzes past conversation history and communicates important interactions with the person being matched by voice. For example, it reminds people of previous appointments or important topics. This allows visually impaired people to check past conversation history and relationships with acquaintances by voice.

[0039] The analysis unit can analyze the movement and speed of an object and provide dynamic distance information. For example, the analysis unit uses a generation AI to analyze the movement and speed of an object and provide dynamic distance information based on that information. For example, it measures the distance to a moving vehicle or pedestrian in real time. The analysis unit also analyzes the speed of the object and adjusts the distance information according to that speed. For example, it issues a prompt warning for fast-moving objects. The analysis unit also analyzes the movement pattern of the object and provides distance information according to that movement. For example, it predicts the movement of an object moving in a zigzag pattern and measures the distance. This allows visually impaired people to obtain distance information to dynamic objects in real time.

[0040] The analysis unit can suggest a safe travel route to the user based on the distance measurement results. For example, the analysis unit uses a generation AI to suggest a safe travel route to the user based on the distance measurement results. For example, it provides audio guidance on a route that avoids obstacles. The analysis unit also analyzes the distance measurement results and suggests the optimal travel route to the user in real time. For example, it provides guidance on a route that avoids crowded areas. The analysis unit also visually displays a safe travel route to the user based on the distance measurement results. For example, it displays the route in cooperation with a smartphone map app. This allows visually impaired people to receive audio guidance on a safe travel route.

[0041] When detecting a foreign object, the analysis unit can analyze the type and danger level of the foreign object and provide detailed information. For example, when the generation AI detects a foreign object, the analysis unit analyzes the type and danger level of the foreign object and provides detailed information. For example, it determines whether a fallen object is dangerous. The analysis unit also analyzes the type of foreign object and issues an appropriate warning to the user based on that information. For example, it issues a warning when it detects glass shards or sharp objects. The analysis unit also analyzes the danger level of the foreign object and adjusts the strength of the warning based on the results. For example, it issues a strong warning for foreign objects that are highly dangerous. This allows visually impaired people to know the type and danger level of the foreign object in detail.

[0042] The analysis unit can suggest avoidance actions to the user based on the foreign object detection results. For example, the analysis unit uses a generation AI to suggest avoidance actions to the user based on the foreign object detection results. For example, it provides voice guidance on the route to avoid the foreign object. The analysis unit also analyzes the location and type of foreign object and suggests specific avoidance actions to the user. For example, it may advise the user to turn right to avoid the foreign object. The analysis unit also suggests avoidance actions to the user in real time based on the foreign object detection results. For example, if the foreign object is moving, it suggests avoidance actions based on that movement. This allows visually impaired people to take appropriate actions to avoid the foreign object.

[0043] The analysis unit can adapt the foreign object detection function to indoor and outdoor environments, enabling foreign object detection in a wide range of situations. For example, the analysis unit uses generative AI to adapt the foreign object detection function to indoor and outdoor environments, enabling foreign object detection in a wide range of situations. For example, it detects indoor furniture and outdoor obstacles. The analysis unit also adjusts the foreign object detection function according to the environment, detecting foreign objects in different situations. For example, it optimizes foreign object detection in dark and bright places. The analysis unit also analyzes indoor and outdoor environmental data and dynamically adjusts the foreign object detection function based on that information. For example, it improves the accuracy of foreign object detection depending on the weather and time of day. This allows visually impaired people to detect foreign objects in a variety of indoor and outdoor environments.

[0044] The analysis unit can suggest an appropriate evacuation route to the user based on the foreign object detection results. For example, the analysis unit uses a generation AI to suggest an appropriate evacuation route to the user based on the foreign object detection results. For example, it provides voice guidance on a safe route to avoid the foreign object. The analysis unit also analyzes the location and type of foreign object and suggests a specific evacuation route to the user. For example, it provides guidance on evacuation routes suitable for emergencies such as fires and earthquakes. The analysis unit also suggests evacuation routes to the user in real time based on the foreign object detection results. For example, if the foreign object is moving, it adjusts the evacuation route according to that movement. This allows visually impaired people to know the appropriate evacuation route to avoid the foreign object.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The analysis unit can analyze weather information from images captured by the camera and provide appropriate advice to the user. For example, if it starts to rain, it will provide audio guidance to advise the user to take an umbrella. The analysis unit can also suggest appropriate clothing to the user based on the weather information. For example, it will advise the user to wear warm clothes on cold days. The analysis unit can also suggest changes to the user's travel route based on the weather information. For example, if roads are slippery due to heavy rain or snow, it will guide the user to an alternative, safer route. This makes it easier for visually impaired people to adapt to changing weather conditions.

[0047] The analysis unit can analyze the user's health condition from the video captured by the camera and provide appropriate advice. For example, it can detect signs of fatigue or stress from the user's facial color and facial expression and provide audio guidance to take a break. The analysis unit can also analyze the user's walking pattern and, if abnormalities are detected, recommend seeing a doctor. For example, if the user's walking becomes unstable, it can suggest visiting a medical institution as soon as possible. The analysis unit can also provide advice on daily life based on the user's health condition. For example, it can suggest appropriate diet and exercise. This makes it easier for visually impaired people to maintain their health.

[0048] The analysis unit can analyze surrounding sound information from the video captured by the camera and provide appropriate advice to the user. For example, it can detect the sound of a car engine or horn and warn the user to be careful when crossing the road. The analysis unit can also suggest appropriate actions to the user based on the surrounding sound information. For example, it can advise the user to use earplugs in noisy places. The analysis unit can also suggest safe travel routes to the user based on the surrounding sound information. For example, it can advise the user to choose a quiet road. This allows visually impaired people to travel safely by utilizing surrounding sound information.

[0049] The analysis unit can analyze the user's posture from the video captured by the camera and provide appropriate advice. For example, if the user has been in the same position for a long time, the analysis unit can provide audio guidance to encourage stretching. The analysis unit can also suggest appropriate sitting and standing postures based on the user's posture. For example, it can advise the user to keep their back straight. The analysis unit can also provide advice for daily life based on the user's posture. For example, it can suggest ways to work with the correct posture. This makes it easier for visually impaired people to maintain healthy posture.

[0050] The analysis unit can analyze the temperature information around the user from the video captured by the camera and provide appropriate advice. For example, if the room temperature is too high, it will provide audio guidance to use the air conditioner. The analysis unit can also suggest appropriate clothing to the user based on the temperature information. For example, it may advise the user to wear light clothing on hot days. The analysis unit can also suggest appropriate actions to the user based on the temperature information. For example, if there is a high risk of heatstroke, it may advise the user to drink more water. This allows visually impaired people to use ambient temperature information to stay comfortable.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: A camera is attached to the glasses and captures images. For example, the camera has high resolution, a wide field of view, and a high frame rate. Step 2: The analysis unit analyzes the video captured by the camera. For example, the analysis unit uses an image processing algorithm to analyze the video and extract information with high accuracy. Step 3: The audio output unit outputs the information analyzed by the analysis unit as audio. For example, the audio output unit generates high-quality audio using speech synthesis technology.

[0053] (Example 2) The visual information support system according to an embodiment of the present invention is a multifunctional system that supports visual information by using AI to analyze images captured by two cameras attached to glasses. This enables the visual information support system to provide multifaceted support for the daily lives of visually impaired people.

[0054] A visual information support system according to an embodiment includes a camera, an analysis unit, and an audio output unit. The camera is attached to glasses and captures video. For example, the camera has high resolution, a wide viewing angle, and a high frame rate. The analysis unit analyzes the video captured by the camera. For example, the analysis unit analyzes the video using an image processing algorithm and extracts information with high accuracy. The audio output unit outputs the information analyzed by the analysis unit as audio. For example, the audio output unit generates high-quality audio using speech synthesis technology. This enables the visual information support system to provide a multifunctional system that supports the daily lives of visually impaired people.

[0055] The analysis unit can analyze text information from video captured by the camera and read it aloud. For example, when the generation AI analyzes text information, the analysis unit understands the context and reads it aloud with appropriate intonation and emotion. For example, it adds a questioning tone to questions and a grateful tone to words of gratitude. The analysis unit also analyzes the structure of sentences and ensures natural reading by taking appropriate pauses for each paragraph. For example, it detects punctuation and line breaks and adds appropriate pauses. The analysis unit also performs sentiment analysis, reading the emotional nuances of the text and reflecting those emotions when reading aloud. For example, it uses a bright tone to express joy and a calm tone to express sadness. This allows visually impaired people to read documents with text information aloud.

[0056] The analysis unit can recognize braille blocks from images captured by the camera and provide audio guidance on their location and direction. For example, when the generation AI analyzes text information, the analysis unit simultaneously analyzes the content of related images and diagrams and explains them audio. For example, it can provide detailed explanations of graphs and diagrams. The analysis unit also uses image recognition technology to identify important elements in images and explain them audio. For example, it can explain people and objects in photographs. The analysis unit can also analyze data in diagrams and read out numerical and statistical information audio. For example, it can explain numbers in tables and data points on graphs. This makes it possible to provide guidance to visually impaired people to move around safely.

[0057] The analysis unit can analyze video of a person captured by a camera and compare it with people registered in advance as learning data. For example, the analysis unit uses a generation AI to analyze the user's emotional state in real time and adjust the speed and tone of the reading based on the results. For example, if the user is relaxed, the reading will be done in a slower tone. The analysis unit also uses an emotion estimation function to emphasize important information and read it earlier if the user is in a hurry. For example, it can quickly convey emergency notifications or important messages. The analysis unit also adjusts the order of the reading content depending on the user's emotional state. For example, if the user is excited, interesting information will be read first. This allows visually impaired people to hear the person matching results when finding acquaintances.

[0058] The analysis unit can analyze the two images captured by the cameras and measure the distance to the object. For example, the analysis unit uses a generative AI to analyze the object's movement and speed and provide dynamic distance information based on that information. For example, it measures the distance to a moving vehicle or pedestrian in real time. The analysis unit also analyzes the object's speed and adjusts the distance information according to that speed. For example, it issues a prompt warning for fast-moving objects. The analysis unit also analyzes the object's movement pattern and provides distance information according to that movement. For example, it predicts the movement of an object moving in a zigzag pattern and measures the distance. This allows visually impaired people to hear the distance to an object when avoiding obstacles.

[0059] The analysis unit can detect objects and situations other than the specified target from the video captured by the camera. For example, when the generation AI detects a foreign object, the analysis unit analyzes the type and danger level of the foreign object and provides detailed information. For example, it determines whether a fallen object is dangerous. The analysis unit also analyzes the type of foreign object and issues an appropriate warning to the user based on that information. For example, it issues a warning if it detects glass shards or a sharp object. The analysis unit also analyzes the danger level of the foreign object and adjusts the strength of the warning based on the results. For example, it issues a strong warning for foreign objects that are highly dangerous. This allows visually impaired people to detect foreign objects while walking and communicate that information via voice.

[0060] When analyzing text information, the analysis unit can understand the context and read it aloud with appropriate intonation and emotion. For example, when the generation AI analyzes text information, the analysis unit understands the context and reads it aloud with appropriate intonation and emotion. For example, it adds a questioning tone to questions and a grateful tone to words of gratitude. The analysis unit also analyzes the structure of the sentence and ensures natural reading by spacing out appropriate pauses for each paragraph. For example, it detects punctuation and line breaks and inserts appropriate pauses. The analysis unit also performs emotion analysis to read the emotional nuances of the text and reflects those emotions when reading aloud. For example, it uses a bright tone to express joy and a calm tone to express sadness. This allows visually impaired people to convey text information in a more natural voice when reading documents.

[0061] In addition to reading out text information, the analysis unit can also explain the contents of related images and charts aloud. For example, when the generation AI analyzes text information, the analysis unit simultaneously analyzes the contents of related images and charts and explains them aloud. For example, it can explain graphs and the contents of diagrams in detail. The analysis unit also uses image recognition technology to identify important elements in images and explain them aloud. For example, it can explain people and objects in photographs. The analysis unit can also analyze chart data and read out numerical and statistical information aloud. For example, it can explain numbers in tables and data points in graphs. This allows the contents of images and charts to be explained aloud when visually impaired people read documents.

[0062] The analysis unit can use the emotion estimation function to adjust the reading speed and tone according to the user's emotional state. For example, the generation AI analyzes the user's emotional state in real time, and adjusts the reading speed and tone based on the results. For example, if the user is relaxed, the analysis unit reads in a slower tone. The analysis unit also uses the emotion estimation function to emphasize important information and read it earlier if the user is in a hurry. For example, it can quickly convey emergency notifications or important messages. The analysis unit also adjusts the order of the content to be read according to the user's emotional state. For example, if the user is excited, it will read information that is of interest first. This allows text information to be conveyed in a more appropriate voice according to the user's emotional state.

[0063] When recognizing the location of a tactile paving block, the analysis unit simultaneously analyzes information about surrounding obstacles and the terrain, allowing it to provide more detailed guidance. For example, when the generation AI recognizes the location of a tactile paving block, the analysis unit simultaneously analyzes information about surrounding obstacles and the terrain, providing detailed guidance. For example, it provides voice guidance about stairs or steps beyond the tactile paving block. When analyzing the location of the tactile paving block, the analysis unit also takes into account information about the surrounding terrain and suggests the optimal route. For example, it provides guidance on routes that avoid slopes and uneven roads. When recognizing the location of a tactile paving block, the analysis unit also analyzes the type and location of surrounding obstacles in detail and provides voice guidance. For example, it detects moving obstacles such as vehicles and bicycles and issues a warning. This makes it possible to provide detailed guidance for visually impaired people to move safely.

[0064] The analysis unit can communicate information to the user using vibrations and tactile feedback in addition to audio guidance. For example, the generation AI communicates information to the user using vibrations and tactile feedback in addition to audio guidance. For example, the location of braille blocks is notified through vibrations. The analysis unit also uses tactile feedback to inform the user of the location and type of obstacle. For example, the strength and pattern of vibrations can be changed to distinguish between different types of obstacles. The analysis unit also combines audio guidance and tactile feedback to provide detailed information to the user. For example, direction guidance is provided through audio and distance is indicated through vibrations. This allows visually impaired people to obtain information not only through audio but also through vibrations and tactile feedback.

[0065] The analysis unit can use the emotion estimation function to adjust the frequency and level of detail of the guidance according to the user's stress level. For example, the generation AI analyzes the user's stress level and adjusts the frequency and level of detail of the guidance based on the results. For example, if the user is feeling stressed, detailed guidance is provided. The analysis unit also uses the emotion estimation function to adjust the content of the guidance according to the user's stress level. For example, if the user is relaxed, brief guidance is provided. The analysis unit also monitors the user's stress level in real time and dynamically adjusts the frequency and level of detail of the guidance according to changes. For example, if stress increases, more frequent guidance is provided. This makes it possible to provide appropriate guidance according to the user's stress level.

[0066] The analysis unit can communicate past conversation history and relationships by voice based on the person matching results. For example, the analysis unit uses a generation AI to communicate past conversation history and relationships by voice based on the matching results. For example, it provides information about previous conversations and mutual acquaintances. The analysis unit also analyzes the relationship between the user and the person being matched based on the matching results and communicates that information by voice. For example, it explains relationships such as between family and friends. The analysis unit also analyzes past conversation history and communicates important interactions with the person being matched by voice. For example, it reminds people of previous appointments or important topics. This allows visually impaired people to check past conversation history and relationships with acquaintances by voice.

[0067] The analysis unit can use the emotion estimation function to analyze the emotions a user has toward a specific person and provide information according to those emotions. For example, the analysis unit uses the generation AI to analyze the user's emotional state and provide information according to the emotions the user has toward a specific person. For example, it can convey good news about a person about whom the user has positive emotions. The analysis unit can also use the emotion estimation function to analyze the emotions the user has toward a specific person and adjust the priority of information based on those emotions. For example, it can convey important information first. The analysis unit can also monitor the user's emotional state in real time and provide information according to changes in that state. For example, if the user is relaxed, it can provide detailed information. This makes it possible to provide appropriate information about a specific person according to the user's emotions.

[0068] The analysis unit can analyze the movement and speed of an object and provide dynamic distance information. For example, the analysis unit uses a generation AI to analyze the movement and speed of an object and provide dynamic distance information based on that information. For example, it measures the distance to a moving vehicle or pedestrian in real time. The analysis unit also analyzes the speed of the object and adjusts the distance information according to that speed. For example, it issues a prompt warning for fast-moving objects. The analysis unit also analyzes the movement pattern of the object and provides distance information according to that movement. For example, it predicts the movement of an object moving in a zigzag pattern and measures the distance. This allows visually impaired people to obtain distance information to dynamic objects in real time.

[0069] The analysis unit can suggest a safe travel route to the user based on the distance measurement results. For example, the analysis unit uses a generation AI to suggest a safe travel route to the user based on the distance measurement results. For example, it provides audio guidance on a route that avoids obstacles. The analysis unit also analyzes the distance measurement results and suggests the optimal travel route to the user in real time. For example, it provides guidance on a route that avoids crowded areas. The analysis unit also visually displays a safe travel route to the user based on the distance measurement results. For example, it displays the route in cooperation with a smartphone map app. This allows visually impaired people to receive audio guidance on a safe travel route.

[0070] The analysis unit can use the emotion estimation function to issue a warning to the user when the distance makes the user feel uneasy. For example, the analysis unit uses a generation AI to analyze the user's emotional state and issue a warning when the distance makes the user feel uneasy. For example, the analysis unit issues an audio warning when the user approaches a distance that makes the user feel uneasy. The analysis unit also uses the emotion estimation function to monitor the distance that makes the user feel uneasy in real time and adjusts the strength of the warning according to the distance. For example, the closer the distance, the louder the warning sound becomes. The analysis unit also issues a visual warning when the distance makes the user feel uneasy based on the user's emotional state. For example, a warning message is displayed on the smartphone screen. This makes it possible to issue an appropriate warning when the distance makes the user feel uneasy.

[0071] When detecting a foreign object, the analysis unit can analyze the type and danger level of the foreign object and provide detailed information. For example, when the generation AI detects a foreign object, the analysis unit analyzes the type and danger level of the foreign object and provides detailed information. For example, it determines whether a fallen object is dangerous. The analysis unit also analyzes the type of foreign object and issues an appropriate warning to the user based on that information. For example, it issues a warning when it detects glass shards or sharp objects. The analysis unit also analyzes the danger level of the foreign object and adjusts the strength of the warning based on the results. For example, it issues a strong warning for foreign objects that are highly dangerous. This allows visually impaired people to know the type and danger level of the foreign object in detail.

[0072] The analysis unit can suggest avoidance actions to the user based on the foreign object detection results. For example, the analysis unit uses a generation AI to suggest avoidance actions to the user based on the foreign object detection results. For example, it provides voice guidance on the route to avoid the foreign object. The analysis unit also analyzes the location and type of foreign object and suggests specific avoidance actions to the user. For example, it may advise the user to turn right to avoid the foreign object. The analysis unit also suggests avoidance actions to the user in real time based on the foreign object detection results. For example, if the foreign object is moving, it suggests avoidance actions based on that movement. This allows visually impaired people to take appropriate actions to avoid the foreign object.

[0073] The analysis unit uses the emotion estimation function to analyze the user's emotions toward foreign objects and issue a warning based on those emotions. For example, the analysis unit uses a generation AI to analyze the user's emotional state and issue a warning based on the user's emotions toward foreign objects. For example, if the user is feeling anxious, a strong warning is issued. The analysis unit also uses the emotion estimation function to analyze the user's emotions toward foreign objects and adjust the content of the warning based on those emotions. For example, if the user is relaxed, a concise warning is issued. The analysis unit also monitors the user's emotional state in real time and dynamically adjusts the strength and content of the warning according to changes. For example, if the user's emotions change, the content of the warning is immediately changed. This makes it possible to issue appropriate warnings about foreign objects based on the user's emotions.

[0074] The analysis unit can adapt the foreign object detection function to indoor and outdoor environments, enabling foreign object detection in a wide range of situations. For example, the analysis unit uses generative AI to adapt the foreign object detection function to indoor and outdoor environments, enabling foreign object detection in a wide range of situations. For example, it detects indoor furniture and outdoor obstacles. The analysis unit also adjusts the foreign object detection function according to the environment, detecting foreign objects in different situations. For example, it optimizes foreign object detection in dark and bright places. The analysis unit also analyzes indoor and outdoor environmental data and dynamically adjusts the foreign object detection function based on that information. For example, it improves the accuracy of foreign object detection depending on the weather and time of day. This allows visually impaired people to detect foreign objects in a variety of indoor and outdoor environments.

[0075] The analysis unit can suggest an appropriate evacuation route to the user based on the foreign object detection results. For example, the analysis unit uses a generation AI to suggest an appropriate evacuation route to the user based on the foreign object detection results. For example, it provides voice guidance on a safe route to avoid the foreign object. The analysis unit also analyzes the location and type of foreign object and suggests a specific evacuation route to the user. For example, it provides guidance on evacuation routes suitable for emergencies such as fires and earthquakes. The analysis unit also suggests evacuation routes to the user in real time based on the foreign object detection results. For example, if the foreign object is moving, it adjusts the evacuation route according to that movement. This allows visually impaired people to know the appropriate evacuation route to avoid the foreign object.

[0076] The analysis unit can use the emotion estimation function to adjust the priority of warnings based on the user's emotions toward foreign objects. For example, the analysis unit uses the generation AI to analyze the user's emotional state and adjust the priority of warnings based on the user's emotions toward foreign objects. For example, the analysis unit prioritizes issuing a warning for foreign objects that cause the user strong anxiety. The analysis unit also uses the emotion estimation function to analyze the user's emotions toward foreign objects and adjust the content of the warning based on those emotions. For example, if the user is relaxed, the analysis unit issues a concise warning. The analysis unit also monitors the user's emotional state in real time and dynamically adjusts the priority of warnings according to changes. For example, if the user's emotions change, the content of the warning is immediately changed. This allows the priority of warnings for foreign objects to be appropriately adjusted according to the user's emotions.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The analysis unit can analyze weather information from images captured by the camera and provide appropriate advice to the user. For example, if it starts to rain, it will provide audio guidance to advise the user to take an umbrella. The analysis unit can also suggest appropriate clothing to the user based on the weather information. For example, it will advise the user to wear warm clothes on cold days. The analysis unit can also suggest changes to the user's travel route based on the weather information. For example, if roads are slippery due to heavy rain or snow, it will guide the user to an alternative, safer route. This makes it easier for visually impaired people to adapt to changing weather conditions.

[0079] The analysis unit can analyze the user's health condition from the video captured by the camera and provide appropriate advice. For example, it can detect signs of fatigue or stress from the user's facial color and facial expression and provide audio guidance to take a break. The analysis unit can also analyze the user's walking pattern and, if abnormalities are detected, recommend seeing a doctor. For example, if the user's walking becomes unstable, it can suggest visiting a medical institution as soon as possible. The analysis unit can also provide advice on daily life based on the user's health condition. For example, it can suggest appropriate diet and exercise. This makes it easier for visually impaired people to maintain their health.

[0080] The analysis unit can analyze surrounding sound information from the video captured by the camera and provide appropriate advice to the user. For example, it can detect the sound of a car engine or horn and warn the user to be careful when crossing the road. The analysis unit can also suggest appropriate actions to the user based on the surrounding sound information. For example, it can advise the user to use earplugs in noisy places. The analysis unit can also suggest safe travel routes to the user based on the surrounding sound information. For example, it can advise the user to choose a quiet road. This allows visually impaired people to travel safely by utilizing surrounding sound information.

[0081] The analysis unit can analyze the user's posture from the video captured by the camera and provide appropriate advice. For example, if the user has been in the same position for a long time, the analysis unit can provide audio guidance to encourage stretching. The analysis unit can also suggest appropriate sitting and standing postures based on the user's posture. For example, it can advise the user to keep their back straight. The analysis unit can also provide advice for daily life based on the user's posture. For example, it can suggest ways to work with the correct posture. This makes it easier for visually impaired people to maintain healthy posture.

[0082] The analysis unit can analyze the temperature information around the user from the video captured by the camera and provide appropriate advice. For example, if the room temperature is too high, it will provide audio guidance to use the air conditioner. The analysis unit can also suggest appropriate clothing to the user based on the temperature information. For example, it may advise the user to wear light clothing on hot days. The analysis unit can also suggest appropriate actions to the user based on the temperature information. For example, if there is a high risk of heatstroke, it may advise the user to drink more water. This allows visually impaired people to use ambient temperature information to stay comfortable.

[0083] The analysis unit can analyze the user's emotional state and suggest music according to that emotion. For example, if the user wants to relax, it will suggest music that has a relaxing effect. The analysis unit can also suggest appropriate entertainment based on the user's emotional state. For example, if the user is feeling stressed, it will suggest movies or books that will help relieve stress. The analysis unit can also suggest appropriate relaxation methods based on the user's emotional state. For example, it can guide the user through meditation or deep breathing techniques. This makes it easier for visually impaired people to relax according to their emotions.

[0084] The analysis unit can analyze the user's emotional state and suggest communication methods based on that emotion. For example, if the user is nervous, it can suggest conversation topics to help them relax. The analysis unit can also provide appropriate interpersonal advice based on the user's emotional state. For example, if the user is feeling angry, it can suggest ways to calm down. The analysis unit can also suggest appropriate timing for communication based on the user's emotional state. For example, it can guide the user to have important conversations when they are relaxed. This makes it easier for visually impaired people to communicate according to their emotions.

[0085] The analysis unit can analyze the user's emotional state and suggest exercises based on that emotion. For example, if the user is feeling stressed, it can suggest exercises that will help relieve stress. The analysis unit can also suggest appropriate exercise intensity based on the user's emotional state. For example, if the user wants to relax, it can suggest light exercise. The analysis unit can also suggest appropriate timing for exercise based on the user's emotional state. For example, it can guide the user to exercise when they feel energized. This makes it easier for visually impaired people to exercise according to their emotions.

[0086] The analysis unit can analyze the user's emotional state and suggest meals based on that emotion. For example, if the user is tired, it can suggest meals to replenish energy. The analysis unit can also suggest appropriate meal times based on the user's emotional state. For example, it can guide the user to eat when they are relaxed. The analysis unit can also suggest appropriate meal contents based on the user's emotional state. For example, if the user is feeling stressed, it can suggest ingredients that will help relieve stress. This makes it easier for visually impaired people to eat meals that suit their emotions.

[0087] The analysis unit can analyze the user's emotional state and suggest resting based on that emotion. For example, if the user is tired, it can suggest an appropriate way to rest. The analysis unit can also suggest appropriate times to rest based on the user's emotional state. For example, it can recommend taking a short break when the user is feeling stressed. The analysis unit can also suggest appropriate environments for resting based on the user's emotional state. For example, it can recommend resting in a quiet place. This makes it easier for visually impaired people to take rest according to their emotions.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: A camera is attached to the glasses and captures images. For example, the camera has high resolution, a wide field of view, and a high frame rate. Step 2: The analysis unit analyzes the video captured by the camera. For example, the analysis unit uses an image processing algorithm to analyze the video and extract information with high accuracy. Step 3: The audio output unit outputs the information analyzed by the analysis unit as audio. For example, the audio output unit generates high-quality audio using speech synthesis technology.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0149] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Two cameras attached to the glasses, an analysis unit that analyzes the video captured by the camera; an audio output unit that outputs the information analyzed by the analysis unit by audio; A system characterized by:

2. The analysis unit Analyzing text information from the image captured by the camera and reading the text information aloud 2. The system of claim 1.

3. The analysis unit The system recognizes the braille blocks from the images captured by the camera and provides audio guidance on their location and direction.

2. The system of claim 1.

4. The analysis unit The image of the person captured by the camera is analyzed and compared with people registered in advance as learning data.

2. The system of claim 1.

5. The analysis unit The two images captured by the cameras are analyzed to measure the distance to the target object.

2. The system of claim 1.

6. The analysis unit Detecting objects and situations other than the specified target from the image captured by the camera 2. The system of claim 1.

Images